Objective: Recent genome-wide association studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow us to identify additional risk loci.

Research design and methods: We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases.

Results: After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top P(adj) = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas.

Conclusions: Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.

Mentions:
A total of 26 pathways reached a nominal P < 0.05, slightly higher than the number expected by chance (0.05 × 439 = 22) (Table 1, Fig. 1). No pathways were associated with type 2 diabetes at P < 0.05 after Bonferroni adjustment (top P = 0.31). A quantile quantile (QQ) plot of the 439 pathways is shown in Fig. 1. The WNT-signaling pathway was the most strongly associated and was of interest because it contains TCF7L2, a common variation that shows the strongest association with type 2 diabetes in all type 2 diabetes GWA studies (7). Excluding the TCF7L2 gene from analysis ranked the WNT-signaling pathway second out of the 439 pathways (nominal P = 0.002). WNT signaling has been proposed as an important candidate pathway for type 2 diabetes, not only because of the TCF7L2 association but because of its potential importance in β-cell development and function (13–15). We therefore investigated the WNT-signaling pathway association using summary-level data from an additional 2,625 case subjects and 2,641 control subjects from the DGI (2) and the FUSION (3) studies. We used the type 2 diabetes association statistics available for each of the 126 WNT-signaling gene SNPs. A QQ plot of the meta-analyzed DGI and FUSION results is shown in Fig. 2. There is modest deviation away from the distribution, but this is within the 95% concentration intervals. Details of the WNT-signaling SNPs reaching P < 0.01 are given in Table 2 and for all 126 SNPs in Supplementary Table 1 (found in an online-only appendix at http://diabetes.diabetesjournals.org/cgi/content/full/db08-1378/DC1). Expanded results are also available for the next best 12 pathways in Supplementary Table 2.

Mentions:
A total of 26 pathways reached a nominal P < 0.05, slightly higher than the number expected by chance (0.05 × 439 = 22) (Table 1, Fig. 1). No pathways were associated with type 2 diabetes at P < 0.05 after Bonferroni adjustment (top P = 0.31). A quantile quantile (QQ) plot of the 439 pathways is shown in Fig. 1. The WNT-signaling pathway was the most strongly associated and was of interest because it contains TCF7L2, a common variation that shows the strongest association with type 2 diabetes in all type 2 diabetes GWA studies (7). Excluding the TCF7L2 gene from analysis ranked the WNT-signaling pathway second out of the 439 pathways (nominal P = 0.002). WNT signaling has been proposed as an important candidate pathway for type 2 diabetes, not only because of the TCF7L2 association but because of its potential importance in β-cell development and function (13–15). We therefore investigated the WNT-signaling pathway association using summary-level data from an additional 2,625 case subjects and 2,641 control subjects from the DGI (2) and the FUSION (3) studies. We used the type 2 diabetes association statistics available for each of the 126 WNT-signaling gene SNPs. A QQ plot of the meta-analyzed DGI and FUSION results is shown in Fig. 2. There is modest deviation away from the distribution, but this is within the 95% concentration intervals. Details of the WNT-signaling SNPs reaching P < 0.01 are given in Table 2 and for all 126 SNPs in Supplementary Table 1 (found in an online-only appendix at http://diabetes.diabetesjournals.org/cgi/content/full/db08-1378/DC1). Expanded results are also available for the next best 12 pathways in Supplementary Table 2.

Bottom Line:
The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations.These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas.Pathway-based approaches to genome-wide association data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.

Objective: Recent genome-wide association studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow us to identify additional risk loci.

Research design and methods: We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases.

Results: After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top P(adj) = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas.

Conclusions: Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.